Computational protein design tools to date have been useful for engineering proteins with a wide range of functions, including DNA binding, co-factor binding, catalysis, fluorescence spectral change, peptide-protein specificity, and protein-protein interaction. In building nanostructures, computational protein design methods have been applied to designing hyperthermophilic proteins, metalloproteins, water-soluble membrane channels, and higher order macromolecular assemblies. Many of these successes rely on fixed backbone approaches that maintain the backbone conformations seen in the original high-resolution crystal structures and focus on remodeling only the sidechains.

Metabolic engineering of microbial strains has been of great interest for producing a wide variety of chemicals including biofuels, polymer precursors, and drugs. While conventional metabolic engineering approaches often focus on modifications to the desired and neighboring pathways, recent developments in computational analysis of metabolic models allow identification of genetic modifications needed to improve production of biochemicals

The outcome of infection by the human immunodeficiency virus-1 (HIV-1, henceforth “HIV” for simplicity) is highly variable across individuals, with time to AIDS ranging from 2 years to more than 20 years [1–4]. Quantifying the fraction of this variability explained by genetic variability in the virus is important to our understanding of the mechanisms of pathogenesis and of the evolution of virulence [5].